This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Formula display:

Abstract

TopHat-Fusion is an algorithm designed to discover transcripts representing fusion
gene products, which result from the breakage and re-joining of two different chromosomes,
or from rearrangements within a chromosome. TopHat-Fusion is an enhanced version of
TopHat, an efficient program that aligns RNA-seq reads without relying on existing
annotation. Because it is independent of gene annotation, TopHat-Fusion can discover
fusion products deriving from known genes, unknown genes and unannotated splice variants
of known genes. Using RNA-seq data from breast and prostate cancer cell lines, we
detected both previously reported and novel fusions with solid supporting evidence.
TopHat-Fusion is available at http://tophat-fusion.sourceforge.net/.webcite

Background

Direct sequencing of messenger RNA transcripts using the RNA-seq protocol [1-3] is rapidly becoming the method of choice for detecting and quantifying all the genes
being expressed in a cell [4]. One advantage of RNA-seq is that, unlike microarray expression techniques, it does
not rely on pre-existing knowledge of gene content, and therefore it can detect entirely
novel genes and novel splice variants of existing genes. In order to detect novel
genes, however, the software used to analyze RNA-seq experiments must be able to align
the transcript sequences anywhere on the genome, without relying on existing annotation.
TopHat [5] was one of the first spliced alignment programs able to perform such ab initio spliced alignment, and in combination with the Cufflinks program [6], it is part of a software analysis suite that can detect and quantify the complete
set of genes captured by an RNA-seq experiment.

In addition to detection of novel genes, RNA-seq has the potential to discover genes
created by complex chromosomal rearrangements. 'Fusion' genes formed by the breakage
and re-joining of two different chromosomes have repeatedly been implicated in the
development of cancer, notably the BCR/ABL1 gene fusion in chronic myeloid leukemia [7-9]. Fusion genes can also be created by the breakage and rearrangement of a single chromosome,
bringing together transcribed sequences that are normally separate. As of early 2011,
the Mitelman database [10] documented nearly 60,000 cases of chromosome aberrations and gene fusions in cancer.
Discovering these fusions via RNA-seq has a distinct advantage over whole-genome sequencing,
due to the fact that in the highly rearranged genomes of some tumor samples, many
rearrangements might be present although only a fraction might alter transcription.
RNA-seq identifies only those chromosomal fusion events that produce transcripts.
It has the further advantage that it allows one to detect multiple alternative splice
variants that might be produced by a fusion event. However, if a fusion involves only
a non-transcribed promoter element, RNA-seq will not detect it.

In order to detect such fusion events, special purpose software is needed for aligning
the relatively short reads from next-generation sequencers. Here we describe a new
method, TopHat-Fusion, designed to capture these events. We demonstrate its effectiveness
on six different cancer cell lines, in each of which it found multiple gene fusion
events, including both known and novel fusions. Although other algorithms for detecting
gene fusions have been described recently [11,12], these methods use unspliced alignment software (for example, Bowtie [13] and ELAND [14]) and rely on finding paired reads that map to either side of a fusion boundary. They
also rely on known annotation, searching known exons for possible fusion boundaries.
In contrast, TopHat-Fusion directly detects individual reads (as well as paired reads)
that span a fusion event, and because it does not rely on annotation, it finds events
involving novel splice variants and entirely novel genes.

Other recent computational methods that have been developed to find fusion genes include
SplitSeek [15], a spliced aligner that maps the two non-overlapping ends of a read (using 21 to
24 base anchors) independently to locate fusion events. This is similar to TopHat-Fusion,
which splits each read into several pieces, but SplitSeek supports only SOLiD reads.
A different strategy is used by Trans-ABySS [16], a de novo transcript assembler, which first uses ABySS [17] to assemble RNA-seq reads into full-length transcripts. After the assembly step,
it then uses BLAT [18] to map the assembled transcripts to detect any that discordantly map across fusion
points. This is a very time-consuming process: it took 350 CPU hours to assemble 147
million reads and > 130 hours for the subsequent mapping step. ShortFuse [19] is similar to TopHat in that it first uses Bowtie to map the reads, but like other
tools it depends on read pairs that map to discordant positions. FusionSeq [20] uses a different alignment program for its initial alignments, but is similar to
TopHat-Fusion in employing a series of sophisticated filters to remove false positives.

We have released the special-purpose algorithms in TopHat-Fusion as a separate package
from TopHat, although some code is shared between the packages. TopHat-Fusion is free,
open source software that can be downloaded from the TopHat-Fusion website [21].

Results

We tested TopHat-Fusion on RNA-seq data from two recent studies of fusion genes: (1)
four breast cancer cell lines (BT474, SKBR3, KPL4, MCF7) described by Edgren et al. [12] and available from the NCBI Sequence Read Archive [SRA:SRP003186]; and (2) the VCaP
prostate cancer cell line and the Universal Human Reference (UHR) cell line, both
from Maher et al. [11]. The data sets contained > 240 million reads, including both paired-end and single-end
reads (Table 1). We mapped all reads to the human genome (UCSC hg19) with TopHat-Fusion, and we
identified the genes involved in each fusion using the RefSeq and Ensembl human annotations.

One of the biggest computational challenges in finding fusion gene products is the
huge number of false positives that result from a straightforward alignment procedure.
This is caused by the numerous repetitive sequences in the genome, which allow many
reads to align to multiple locations on the genome. To address this problem, we developed
strict filtering routines to eliminate the vast majority of spurious alignments (see
Materials and methods). These filters allowed us to reduce the number of fusions reported
by the algorithm from > 100,000 to just a few dozen, all of which had strong support
from multiple reads.

Overall, TopHat-Fusion found 76 fusion genes in the four breast cancer cell lines
(Table 2; Additional file 1) and 19 in the prostate cancer (VCaP) cell line (Table 3; Additional file 2). In the breast cancer data, TopHat-Fusion found 25 out of the 27 previously reported
fusions [12]. Of the two fusions TopHat-Fusion missed (DHX35-ITCH, NFS1-PREX1), DHX35-ITCH was
included in the initial output, but was filtered out because it was supported by only
one singleton read and one mate pair. The remaining 51 fusion genes were not previously
reported. In the VCaP data, TopHat-Fusion found 9 of the 11 fusions reported previously
[11] plus 10 novel fusions. One of the missing fusions involved two overlapping genes,
ZNF577 and ZNF649 on chromosome 19, which appears to be read-through transcription
rather than a true gene fusion.

Figure 1 illustrates two of the fusion genes identified by TopHat-Fusion. Figure 1a shows the reads spanning a fusion between the BCAS3 (breast carcinoma amplified sequence 3) gene on chromosome 17 (17q23) and the BCAS4 gene on chromosome 20 (20q13), originally found in the MCF7 cell line in 2002 [22]. As illustrated in the figure, many reads clearly span the boundary of the fusion
between chromosomes 20 and 17, illustrating the single-base precision enabled by TopHat-Fusion.
Figure 1b shows a novel intra-chromosomal fusion product with similarly strong alignment evidence
that TopHat-Fusion found in BT474 cells. This fusion merges two genes that are 13
megabases apart on chromosome 17: TOB1 (transducer of ERBB2, ENSG00000141232) at approximately 48.9 Mb; and SYNRG (synergin gamma) at approximately 35.9 Mb.

Figure 1.Read distributions around two fusions: BCAS4-BCAS3 and TOB1-SYNRG. (a) Sixty reads aligned by TopHat-Fusion that identify a fusion product formed by the
BCAS4 gene on chromosome 20 and the BCAS3 gene on chromosome 17. The data contained more reads than shown; they are collapsed
to illustrate how well they are distributed. The inset figures show the coverage depth
in 600-bp windows around each fusion. (b) TOB1 (ENSG00000141232)-SYNRG is a novel fusion gene found by TopHat-Fusion, shown here with 70 reads mapping across
the fusion point. Note that some of the reads in green span an intron (indicated by
thin horizontal lines extending to the right), a feature that can be detected by TopHat's
spliced alignment procedure.

Single versus paired-end reads

Using four known fusion genes (GAS6-RASA3, BCR-ABL1, ARFGEF2-SULF2, and BCAS4-BCAS3), we compared TopHat-Fusion's results using single and paired-end reads from the
UHR data set (Table 4). All four fusions were detected using either type of input data. Although Maher
et al. [11] reported much greater sensitivity using paired reads, we found that the ability to
detect fusions using single-end reads, when used with TopHat-Fusion, was sometimes
nearly as good as with paired reads. For example, the reads aligning to the BCR-ABL1 fusion provided similar support using either single or paired-end data (Additional
file 3). Among the top 20 fusion genes in the UHR data, 3 had more support from single-end
reads and 9 had better support from paired-end reads (Additional file 4). Note that longer reads might be more effective for detecting gene fusions from
unpaired reads: Zhao et al. [23] found 4 inter-chromosomal and 3 intra-chromosomal fusions in a breast cancer cell
line (HCC1954), using 510,703 relatively long reads (average 254 bp) sequenced using
454 pyrosequencing technology. Very recently, the FusionMap system [24] was reported to achieve better results, using simulated 75-bp reads, on single-end
versus paired-end reads when the inner mate distance is short.

Table 4. Comparisons of results from using single-end and paired-end reads for finding fusions

Additional file 4.Table S3 - the top 20 fusion candidates reported by TopHat-Fusion in the UHR data. The top 20 fusion genes from the Universal Human Reference (UHR) data found by TopHat-Fusion,
sorted by the scoring scheme described in Figure 6. Single- and paired-end reads were used separately in order to compare TopHat's ability
to find fusions using only single-end reads.

Estimate of the false positive rate

In order to estimate the false positive rate of TopHat-Fusion, we ran it on RNA-seq
data from normal human tissue, in which fusion transcripts should be absent. Using
paired-end RNA-seq reads from two tissue samples (testes and thyroid) from the Illumina
Body Map 2.0 data [ENA: ERP000546] (see [25] for the download web page), the system reported just one and nine fusion transcripts
in the two samples, respectively. Considering that each sample comprised approximately
163 million reads, and assuming that all reported fusions are false positives, the
false positive rate would be approximately 1 per 32 million reads. Some of the reported
fusions may in fact be chimeric sequences due to ligation of cDNA fragments [26], which would make the false positive rate even lower. For this experiment, we required
five spanning reads and five supporting mate pairs because the number of reads is
much higher than those of our other test samples. When the filtering parameters are
changed to one read and two mate pairs, TopHat-Fusion predicts 4 and 43 fusion transcripts
in the two samples, respectively (Additional file 5).

Because it is also a standalone fusion detection system, we ran FusionSeq (0.7.0)
[20] on one of our data sets to compare its performance to TopHat-Fusion. FusionSeq consists
of two main steps: (1) identifying potential fusions based on paired-end mappings;
and (2) filtering out fusions with a sophisticated filtration cascade containing more
than ten filters. Using the breast cancer cell line MCF7, in which three true fusions
(BCAS4-BCAS3, ARFGEF2-SULF2, RPS6KB1-TMEM49) were previously reported, we ran FusionSeq with mappings from Bowtie that included
discordantly mapped mate pairs. (Note that FusionSeq was designed to use the commercial
ELAND aligner, but we used the open-source Bowtie instead.) To do this, we aligned
each end of every mate pair separately, allowing them to be aligned to at most two
places, and then combined and converted them to the input format required by FusionSeq.

When we required at least two supporting mate pairs for a fusion (the same requirement
as for our TopHat-Fusion analysis), FusionSeq missed one true fusion (RPS6KB1-TMEM49) because it was supported by only one mate pair. In contrast, TopHat-Fusion found
this fusion because it was supported by three mate pairs from TopHat-Fusion's alignment
algorithm: one mate pair contains a read that spans a splice junction, and the other
contains a read that spans a fusion point. These spliced alignments are not found
by Bowtie or ELAND. With this spliced mapping capability, TopHat-Fusion will be expected
to have higher sensitivity than those based on non-gapped aligners. When the minimum
number of mate pairs is reduced to 1, FusionSeq found all three known fusions at the
expense of increased running time (9 hours versus just over 2 hours) and a large increase
in the number of candidate fusions reported (32,646 versus 5,649).

Next, we ran all of FusionSeq's filters except two (PCR filter and annotation consistency
filter) that would otherwise eliminate two of the true fusions. FusionSeq reported
14,510 gene fusions (Additional file 6), compared to just 14 fusions reported by TopHat-Fusion (Additional file 7), where both found the three known fusions. Among those fusions reported by FusionSeq,
13,631 and 276 were classified as inter-chromosomal and intra-chromosomal, respectively.
When we used all of FusionSeq's filters, it reported 763 candidate fusions that include
only one of the three known fusions.

FusionSeq reports three scores for each transcript: SPER (normalized number of inter-transcript
paired-end reads), DASPER (difference between observed and expected SPER), and RESPER
(ratio of observed SPER to the average of all SPERs). Because RESPER is proportional
to SPER in the same data, we used SPER and DASPER to control the number of fusion
candidates: ARFGEF2-SULF2 (SPER, 1.289452; DASPER, 1.279144), BCAS4-BCAS3 (0.483544, 0.482379), and RPS6KB1-TMEM49 (0.161181, 0.133692). First, we used SPER of 0.161181 and DASPER of 0.133692 to find
the minimum set of fusion candidates that include the three known gene fusions. This
reduced the number of candidates from 14,510 to 11,774. Second, we used the SPER and
DASPER values from ARFGEF2-SULF2 and BCAS4-BCAS3, which resulted in 1,269 and 512 predicted fusions, respectively.

We next compared TopHat-Fusion with deFuse (0.4.2) [27]. deFuse maps read pairs against the genome and against cDNA sequences using Bowtie,
and then uses discordantly mapped mate pairs to find candidate regions where fusion
break points may lie. This allows detection of break points at base-pair resolution,
similar to TopHat-Fusion. After collecting sequences around fusion points, it maps
them against the genome, cDNAs, and expressed sequence tags using BLAT; this step
dominates the run time.

Using two data sets - MCF7 and SKBR3 - we ran both TopHat-Fusion and deFuse using
the following matched parameters: one minimum spanning read, two supporting mate pairs,
and 13 bp as the anchor length. For the MCF7 cell line, both programs found the three
known fusion transcripts. For the SKBR3 cell line, both programs found the same seven
fusions out of nine previously reported fusion transcripts (one known fusion, CSE1L-ENSG00000236127, was not considered because ENSG00000236127 has been removed from
the recent Ensembl database). Both programs missed two fusion transcripts: DHX35-ITCH and NFS1-PREX1. However, TopHat-Fusion had far fewer false positives: it predicted 42 fusions in
total, while deFuse predicted 1,670 (Additional files 7, 8 and 9).

Table 5 shows the number of spanning reads and supporting pairs detected by TopHat-Fusion
and deFuse, respectively, for ten known fusions in SKBR3 and MCF7. The numbers are
similar in both programs for the known fusion transcripts. Considering the fact TopHat-Fusion's
mapping step does not use annotations while deFuse does, this result illustrates that
TopHat-Fusion can be highly sensitive without relying on annotations. Finally, we
noted that TopHat-Fusion was approximately three times faster: for the SKBR3 cell
line, it took 7 hours, while deFuse took 22 hours, both using the same eight-core
computer.

Table 5. Comparisons of TopHat-Fusion and deFuse for SKBR3 and MCF7 cell lines

Unlike FusionSeq and deFuse (as well as other fusion-finding programs), one of the
most powerful features in TopHat-Fusion is its ability to map reads across introns,
indels, and fusion points in an efficient way and report the alignments in a modified
SAM (Sequence Alignment/Map) format [28].

Conclusions

Unlike previous approaches based on discordantly mapping paired reads and known gene
annotations, TopHat-Fusion can find either individual or paired reads that span gene
fusions, and it runs independently of known genes. These capabilities increase its
sensitivity and allow it to find fusions that include novel genes and novel splice
variants of known genes. In experiments using multiple cell lines from previous studies,
TopHat-Fusion identified 34 of 38 previously known fusions. It also found 61 fusion
genes not previously reported in those data, each of which had solid support from
multiple reads or pairs of reads.

Materials and methods

The first step in analysis of an RNA-seq data set is to align (map) the reads to the
genome, which is complicated by the presence of introns. Because introns can be very
long, particularly in mammalian genomes, the alignment program must be capable of
aligning a read in two or more pieces that can be widely separated on a chromosome.
The size of RNA-seq data sets, numbering in the tens of millions or even hundreds
of millions of reads, demands that spliced alignment programs also be very efficient.
The TopHat program achieves efficiency primarily through the use of the Bowtie aligner
[13], an extremely fast and memory-efficient program for aligning unspliced reads to the
genome. TopHat uses Bowtie to find all reads that align entirely within exons, and
creates a set of partial exons from these alignments. It then creates hypothetical
intron boundaries between the partial exons, and uses Bowtie to re-align the initially
unmapped (IUM) reads and find those that define introns.

TopHat-Fusion implements several major changes to the original TopHat algorithm, all
designed to enable discovery of fusion transcripts (Figure 2). After identifying the set of IUM reads, it splits each read into multiple 25-bp
pieces, with the final segment being 25 bp or longer; for example, an 80-bp read will
be split into three segments of length 25, 25, and 30 (Figure 3).

Figure 2.TopHat-Fusion pipeline. TopHat-Fusion consists of two main modules: (1) finding candidate fusions and aligning
reads across them; and (2) filtering out false fusions using a series of post-processing
routines.

Figure 3.Aligning a read that spans a fusion point. (a) An initially unmapped read of 75 bp is split into three segments of 25 bp, each of
which is mapped separately. As shown here, the left (red) and right (blue) segments
are mapped to two different chromosomes, i and j. (b) The unmapped green segment is used to find the precise fusion point between i and
j. This is done by aligning the green segment to the sequences just to the right of
the red segment on chromosome i and just to the left of the blue segment on chromosome
j.

The algorithm then uses Bowtie to map the 25-bp segments to the genome. For normal
transcripts, the TopHat algorithm requires that segments must align in a pattern consistent
with introns; that is, the segments may be separated by a user-defined maximum intron
length, and they must align in the same orientation along the same chromosome. For
fusion transcripts, TopHat-Fusion relaxes both these constraints, allowing it to detect
fusions across chromosomes as well as fusions caused by inversions.

Following the mapping step, we filter out candidate fusion events involving multi-copy
genes or other repetitive sequences, on the assumption that these sequences cause
mapping artifacts. However, some multi-mapped reads (reads that align to multiple
locations) might correspond to genuine fusions: for example, in Kinsella et al. [19], the known fusion genes HOMEZ-MYH6 and KIAA1267-ARL17A were supported by 2 and 11 multi-mapped read pairs, respectively. Therefore, instead
of eliminating all multi-mapped reads, we impose an upper bound M (default M = 2) on the number of mappings per read. If a read or a pair of reads has
M or fewer multi-mappings, then all mappings for that read are considered. Reads with
> M mappings are discarded.

To further reduce the likelihood of false positives, we require that each read mapping
across a fusion point have at least 13 bases matching on both sides of the fusion,
with no more than two mismatches. We consider alignments to be fusion candidates when
the two 'sides' of the event either (a) reside on different chromosomes or (b) reside
on the same chromosome and are separated by at least 100,000 bp. The latter are the
results of intra-chromosomal rearrangements or possibly read-through transcription
events. We chose the 100,000-bp minimum distance as a compromise that allows TopHat-Fusion
to detect intra-chromosomal rearrangements while excluding most but not all read-through
transcripts. Intra-chromosomal fusions may also include inversions.

As shown in Figure 3a, after splitting an IUM read into three segments, the first and last segments might
be mapped to two different chromosomes. Once this pattern of alignment is detected,
the algorithm uses the three segments from the IUM read to find the fusion point.
After finding the precise location, the segments are re-aligned, moving inward from
the left and right boundaries of the original DNA fragment. The resulting mappings
are combined together to give full read alignments. For this re-mapping step, TopHat-Fusion
extracts 22 bp immediately flanking each fusion point and concatenates them to create
44-bp 'spliced fusion contigs' (Figure 4a). It then creates a Bowtie index (using the bowtie-build program [13]) from the spliced contigs. Using this index, it runs Bowtie to align all the segments
of all IUM reads against the spliced fusion contigs. For a 25-bp segment to be mapped
to a 44-bp contig, it has to span the fusion point by at least 3 bp. (For more details,
see Additional files 10, 11 and 12.)

Figure 4.Mapping against fusion points and selecting best read alignments. (a) Bowtie is used to align all segments from the initially unmapped (IUM) reads against
spliced fusion contigs, shown in gray on the right. For example, the brown read on
the top left aligns to the first spliced fusion contig on the top right. (b) IUM reads 1 and 2 each have multiple alignments. Read 1 has a gap-free alignment,
shown in dark blue, which is preferred over the other two alignments shown in lighter
shades of blue. The gap-free alignment with three mismatches is preferred over the
fusion alignment with one mismatch. If all alignments have gaps and mismatches, then
the algorithm prefers those with fewer mismatches, as shown by the dark green alignment
for IUM read 2. Full details of the scoring function that determines these preferences
are described in the Materials and methods.

Additional file 11.Figure S2 - Finding fusions using two segments and partner reads in paired-end reads. (a) TopHat allows one to three mismatches when mapping segments using Bowtie, which enables
segments to be mapped even if a few bases cross a fusion point (the last two bases
of the red segment, GG). These two segments, mapped to two different chromosomes,
are used to identify a fusion point. (b) For paired-end reads, the mapped position of the partner read is used to narrow down
the range of a fusion point. The second segment (shown in green) cannot be mapped
because it spans a fusion point. Here, its partner read is mapped and the fusion point
is likely to be located within the inner mate distance ± standard deviation of the
left genomic coordinate of the partner read. TopHat-Fusion is able to use this relatively
small range to efficiently map the right part of the second segment to the right side
of a fusion (case 2). The left part of the second segment is aligned to the right
side of the mapped first segment (case 3).

Additional file 12.Figure S3 - stitching segments to produce a full read alignment. (a) The segment in the third row for segment 1 and the one in the first row for segment
2 are connected because they are on the same chromosome (i) in the forward direction
and with adjacent coordinates. These are then matched to the second row in segment
3 and glued together, producing the full-length read alignment at the bottom. (b) TopHat-Fusion tries to connect the segment in the second row for segment 1 with segments
in the first and second rows for segment 2, but neither succeeds. Case 1 would require
two fusion points in the same read, and case 2 cannot be fused with consistent coordinates.
(c) Attempts to connect the segment in the second row for segment 2 with the one in the
first row in segment 3: in case 3, there is no intron available, there is no fusion
in case 4, and case 5 would require more than one fusion.

After stitching together the segment mappings to produce full alignments, we collect
those reads that have at least one alignment spanning the entire read. We then choose
the best alignment for each read using a heuristic scoring function, defined below.
We assign penalties for alignments that span introns (-2), indels (-4), or fusions
(-4). For each potential fusion, we require that spanning reads have at least 13 bp
aligned on both sides of the fusion point. (This requirement alone eliminates many
false positives.) After applying the penalties, if a read has more than one alignment
with the same minimum penalty score, then the read with the fewest mismatches is selected.
For example, in Figure 4b, IUM read 1 (in blue) is aligned to three different locations: (1) chromosome i with no gap, (2) chromosome j where it spans an intron, and (3) a fusion contig formed between chromosome m and chromosome n. Our scoring function prefers (1), followed by (2), and by (3). For IUM read 2 (Figure
4b, in green), we have two alignments: (1) a fusion formed between chromosome i and chromosome j, and (2) an alignment to chromosome k with a small deletion. These two alignments both incur the same penalty, but we select
(1) because it has fewer mismatches.

We imposed further filters for each data set: (1) in the breast cancer cell lines
(BT474, SKBR3, KPL4, MCF7), we required two supporting pairs and the sum of spanning
reads and supporting pairs to be at least 5; (2) in the VCaP paired-end reads, we
required the sum of spanning reads and supporting pairs to be at least 10; (3) in
the UHR paired-end reads, we required (i) three spanning reads and two supporting
pairs or (ii) the sum of spanning reads and supporting pairs to be at least 10; and
(4) in the UHR single-end reads, we required two spanning reads. These numbers were
determined empirically using known fusions as a quality control. All candidates that
fail to satisfy these filters were eliminated.

In order to remove false positive fusions caused by repeats, we extract the two 23-base
sequences spanning each fusion point and then map them against the entire human genome.
We convert the resulting alignments into a list of pairs (chromosome name, genomic
coordinate - for example, chr14:374384). For each 23-mer adjacent to a fusion point,
we test to determine if the other 23-mer occurs within 100,000 bp on the same chromosome.
If so, then it is likely a repeat and we eliminate the fusion candidate. We further
require that at least one side of a fusion contains an annotated gene (based on known
genes from RefSeq), otherwise the fusion is filtered out. These steps alone reduced
the number of fusion candidates in our experiments from 105 to just a few hundred.

As reported in Edgren et al. [12], true fusion transcripts have reads mapping uniformly in a wide window across the
fusion point, whereas false positive fusions are narrowly covered. Using this idea,
TopHat-Fusion examines a 600-bp window around each fusion (300-bp each side), and
rejects fusion candidates for which the reads fail to cover this window (Figure 5b). The final process is to sort fusions based on how well-distributed the reads are
(Figure 6). The scoring scheme prefers alignments that have no gaps (or small gaps) and uniform
depth.

Figure 5.Supporting and contradicting evidence for fusion transcripts. (a) Given a fusion point and the chromosomes (gray) spanning it, single-end and paired-end
reads (blue) support the fusion. Other reads (red) contradict the fusion by mapping
entirely to either of the two chromosomes. (b) TopHat-Fusion prefers reads that uniformly cover a 600-bp window centered in any fusion
point. On the upper left, blue reads cover the entire window. On the lower left, red
reads cover only a narrow window around the fusion. On the lower right, reads do not
cover part of the 600-bp window. The cases shown in orange will be rejected by TopHat-Fusion.

Figure 6.TopHat-Fusion's scoring scheme of read distributions. A scoring scheme of how well distributed reads are around a fusion point; these
result scores are used to sort the list of candidate fusions. Variables are defined
in the main text.

Even with strict parameters for the initial alignment, many of the segments will map
to multiple locations, which can make it appear that a read spans two chromosomes.
Thus the algorithm may find large numbers of false positives, primarily due to the
presence of millions of repetitive sequences in the human genome. Even after filtering
to choose the best alignment per read, the experiments reported here yielded initial
sets of about 400,000 and 135,000 fusion gene candidates from the breast cancer (BT474,
SKBR3, KPL4, MCF7) and prostate cancer (VCaP) cell lines, respectively. The additional
filtering steps eliminated the vast majority of these false positives, reducing the
output to 76 and 19 fusion candidates, respectively, all of which have strong supporting
evidence (Tables 2 and 3).

The scoring function used to rank fusion candidates uses the number of paired reads
in which the reads map on either side of the fusion point in a consistent orientation
(Figure 5a) as well as the number of reads in conflict with the fusion point. Conflicting reads
align entirely to either of the two chromosomes and span the point at which the chromosome
break should occur (Figure 5b).

The overall fusion score is computed as:

where lcount is the number of bases covered in a 300-bp window on the left (Figure
6), lavg is the average read coverage on the left, max_avg is 300, lgap is the length
of any gap on the left, rate is the ratio between the number of supporting mate pairs
and the number of contradicting reads, |lavg - ravg| is a penalty for expression differences
on either side of the fusion, and dist is the sum of distances between each end of
a pair and a fusion. (For single-end reads, the rate uses spanning reads rather than
mate pairs.) The variance in coverage lder is:

where lwindow is the size of the left window (300 bp).

TopHat-Fusion outputs alignments of singleton reads and paired-end reads mapped across
fusion points in SAM format [28], enabling further downstream analyses [29], such as transcript assembly and differential gene expression. The parameters in
the filtering steps can be changed as needed for a particular data set.

Authors' contributions

DK developed the TopHat-Fusion algorithms, performed the analysis and discussed the
results, implemented TopHat-Fusion and wrote the manuscript. SLS developed the TopHat-Fusion
algorithms, performed the analysis and discussed the results, and wrote the manuscript.
All authors have read and approved the manuscript for publication.

Acknowledgements

We would like to thank Christopher Maher and Arul Chinnaiyan for providing us with
their RNA-seq data. Thanks to Lou Staudt for invaluable feedback on early versions
of TopHat-Fusion, to Ryan Kelley for his indel-finding algorithm, and to Geo Pertea
for sharing his scripts and help with TopHat's development. This work was supported
in part by NIH grants R01-LM006845 and R01-HG006102.